Trent E.M., Wright P.K. Metal Cutting

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ARTIFICIAL INTELLIGENCE BASED MODELING 403

Data from the many examples in Chapters 5 and 9 have then been averaged over a range of

cutting conditions. For cutting steels at commercial speeds and feeds, the results show that a

starting heuristic to use for steels is:

= 0.76 (12. 28)

= 1.18 (12. 29)

These heuristic constants can thus be used to convert the remote thermocouple reading (see its

position in Figure 2.1) directly to the edge or maximum temperature. The cutting-edge tempera-

ture can be used to control wear by plastic deformation, and the maximum temperature used to

control wear by diffusion. It is re-emphasized that this is an AI-based approach. The starting

values for the heuristics (values of 0.76 and 1.18) are deliberately approximate. However, over

time, the idea is that these will be adjusted from the learning that goes on during normal opera-

tions of the machine.

12.6.7.1 Stress monitoring

Piezo-electric dynamometers and strain-gauged tool-holders are, today, the most practical

devices for obtaining the raw, cutting force data. Although it is relatively easy to measure the

forces, there is no direct relationship between them and the physics of the chip formation pro-

cess (see Chapter 4). Actually, the simple tensile test is the best illustration that forces are mean-

ingless unless they are related to the area under load. Thus, to exploit the forces, the chip-tool

contact area a must be specified, and then the stress can be calculated ( = F/a). Actually,

this is the same as in any other engineering design situation.

To correlate forces with mean stresses during real-time machining, it is possible to use con-

trolled contact area tools. These predetermine the value of the area that bears the load. Of

course, these tools are mainly designed to provide the practical advantages such as chip-shape

control and the overall reduction in cutting temperatures. To implement the AI-based model, the

mean stress can be converted to the maximum stress on the tool edge, to allow wear by plastic

deformation to be controlled. The results in Chapter 4 show that for many cutting conditions and

work materials, a conservative starting point for the heuristic is to use the lower bound of the

experimental stress variations:

= (12. 30)

The result has been used in the real-time determination of the stress acting on the edge of the

tool. {Wear by fracture is also strongly influenced by the maximum stress acting on the tool

edge.}

12.6.7.2 Summary

To reiterate, this AI-based control scheme establishes the “cruising speed” for cutting where

rates of metal removal are “safe but productive”. The model has been illustrated by analyzing

edge

mean

max

mean

edge

max

1.75σ

mean

404 MODELING OF METAL CUTTING

one of the major wear mechanisms, plastic deformation of the cutting edge. Thermocouples and

dynamometers can be used for real-time machining to ensure that operations remain safe, but

productive in the shaded area of Figure 12.16. In this way, the inevitable approximations in the

modeling and the unpredictable variations in actual machining can be accounted for by the sen-

sor feedback (see Figure 2.1)

12.7 CONCLUSIONS

In this chapter, the division by section heading is more to create some structure and to identify

certain “styles of approach”. It pays not to “label” any of the methods because there is consider-

able overlap between all the modeling methods reviewed.

Actually, Taylor was a complete empiricist and did just focus on collecting data for later use.

His famous equation came from replotting has experimental data on log-log paper. Armarego et

al’s work is also empirical at first glance but they base their equations on the mechanics of chip

flow and there is a great deal of thought behind the way the equations are set up.

The Merchant model remains the clearest physical picture of the metal cutting problem. But

even this needs an empirically derived coefficient of friction. Oxley and Rowe-Spick had the

goal of improving Merchant’s model and worked to find closed-form predictive analyses for

forces and temperatures. Again, the analyses provide new insights but both use empirical con-

stants to calculate the final value of the shear plane angle.

The mechanistic models do not attempt (unlike Merchant/Oxley/Rowe-Spick) to predict the

shear plane angle. In addition, they rely on empirical constants such as K

’ and K

in equations

12.5 and 12.6. DeVor et al’s work seems mechanistic at first glance but it also includes the

empirical constants K

´ and K

. Recent experiments by Stori and Wright are displayed on the

following Website <http://madmax.me.berkeley.edu/webparam/demo2/summary2.html>. These

show very good agreement between the predictions and values obtained from a coordinate mea-

suring machine.

The finite element models in metal cutting provide a more global view of the calculation and

they now allow tool designs and cutting conditions to be evaluated as shown in the three Case

Studies in section 12.5. Chen and Black’s review paper provides an excellent summary of the

early FEA methods.

47-52 and 72-82

This is reproduced below with some recent additions. The AI-

based model allows for a different approach where approximate heuristics from the machining

research are used to operate a machine tool in a “safe but productive” region. Finally, Molecular

Dynamic simulation is mentioned here as another fruitful avenue to pursue. Komanduri and col-

leagues, and Ueda and colleagues, are cited as sources for such information.

84-86

CONCLUSIONS 405

TABLE 12.2 FEA models in metal cutting (Courtesy of J.T. Black and Z.G.Chen).

YEAR FIELD AUTHORS REF. COMMENTS

1973 stress Klamecki [47] 3-D, incipient chip formation

1973 temperature Tay [27] Steady state temperature

1974 temperature Tay et al. [48] Steady state temperature

1979 temperature Muraka et al. [52] Steady state temperature

1980 residual stress Lajczok [72] Steady state temperature

1982 stress & temp Usui & Shirakashi [73] Steady state temperature

1983 temperature Stevenson et al. [51] Comparison with experiment

1984 stress Iwata et al. [74] Temperature

1984 instant. stress Liu et al. [76] Thermal & mechanical

1985 stress Strenkowski and

Carroll

[80] Incipient stage to steady state

1986 stress Strenkowski and

Carroll

[81] Eulerian model

1988 stress Carroll and Stren-

kowski

[82] Lagrangian & Eulerian models

1988 temperature Childs et al. [53] Effects of coolant

1988 stress & temp Klamecki & Kim [77] 3-D, incipient chip formation

1989 stress & temp Howerton & Stren-

kowski

[78] Eulerian model, BUE formation

1990 stress & temp Strenkowski and

Moon

[57] Modified Eulerian model

1990 stress & temp Strenkowski and

Hiatt

[56] Eulerian model, ductile/brittle

regime

1991 stress & temp Strenkowski, Lar-

son and Chern

[61] Eulerian model, tool wear

1991 stress & temp Komvopoulos et al. [55] Lagrangian

1992 stress & temp Lin & Lin [56] Lagrangian

1994 3D stresses Strenkowski and Lin [79] 3D tool forces and chip flow

1997 stress & temp Athavale & Stren-

kowski

[37] Material damage model

1997 tool stresses Kistler [64] Comparisons with experiment

1998 stress & temp Jawahir et al [46] Collection of modeling papers

including many FEAs

406 MODELING OF METAL CUTTING

12.8 REFERENCES

1. Taylor, F.W., Trans. A.S.M.E., 28, 31 (1907)

2. Merchant, M.E., J. Appl. Phys., 16, (5), 267 (1945)

3. Devor, R.E., Kline, W.A., and Zdeblick, W.J., Eight North American Manufacturing Research

Conference Proceedings, p. 297, (1980)

4. Sandstrom D.R, and Hodowany, J.N., Proceedings of the CIRP International Workshop on

Modeling of Machining Operations, held in Atlanta, GA., Published by the University of

Kentucky Lexington, KY, 40506-0108. p.217, (1998)

5. Wright, P.K. and Bourne, D.A., Manufacturing Intelligence, Addison Wesley, (1988)

6. ASM Metals Handbook, Volume 3 - Machining

7. Metcut Research Associates, Machining Data Handbook, 3rd Edn. (1980).

8. Armarego, E.J.A, Smith, A.R.J. and Gong, Z.J., Annals of CIRP, 39 (1) pp. 41-45 (1990

9. Armarego, E.J.A. and Zhao, H., Annals of CIRP, 45 (1) pp. 65-70 (1996).

10. Samaranayake, P. and Armarego, E.J.A., Pacific Conference on Manufacturing (PCM ‘98)

(1998).

11. Armarego, E.J.A. and Wang, J. 4th Int. Conf. on Advanced Manufacturing Systems and Tech-

nology, Udine, Italy, pp. 97-105, (1996).

12. Armarego, E.J.A., Proceedings of the CIRP International Workshop on Modeling of Machin-

ing Operations, held in Atlanta, GA., Published by the University of Kentucky Lexington,

KY, 40506-0108. p.95, (1998)

13. Sandstrom, D., <http://www.halcyon.com/sandstr/ApTool.html>

14. Koenigsberger, F. and Sabberwal, A.J.P., Int. J. MTDR, Vol. 1, pp. 15-33, (1961).

15. Stori, J.A., Machining Operation Planning based on Process Simulation and the Mechanics

of Milling, Ph.D., thesis University of California, Berkeley, See pages 40-54 in particu-

lar(1998)

16. J.A. Stori, C.King and P.K. Wright,” ASME Journal of Manufacturing Science and Engineer-

ing, (1999)

17. S.G. Kapoor, DeVor, R.E., Zhu, R., Gajjela, R., Parakkai, G., and Smithey, D., Proceedings

of the CIRP International Workshop on Modeling of Machining Operations, held in Atlanta,

GA., Published by the University of Kentucky Lexington, KY, 40506-0108. p.109, (1998)

18. Kline, W.A., DeVor, R.E., and Lindberg, J.R.,” Int. J. Mach. Tool Des. Res., Vol. 22, No. 1.

pp. 7-22 1982.

19. Kline, W.A., DeVor, R.E., and Shareef, I.A., ASME Journal of Engineering for Industry, Vol.

104, Aug. 1982, p. 272.

20. Sutherland, J.W., and DeVor, R.E., ASME Journal of Engineering for Industry, Vol. 108,

Nov. 1986, p. 269.

21. Ehmann,K.F., Kapoor, S.G., DeVor, R.E., Lagoglu., I., Journal of Manufacturing Science

and Engineering, 119, 655, (1997)

22. Babin, T.S., The Modeling, Characterization, and Assessment of End Milled Surfaces, Ph.D.

Dissertation, University of Illinois at Urbana-Champaign, 1988.

23. Babin, T.S., Lee, J.M., Sutherland, J.W., and Kapoor, S.G., 1985, Proceedings 13th North

American Manufacturing Research Conference, pp. 362-368.

REFERENCES 407

24. Melkote, S.N. and Thangaraj, A.R., ASME Journal of Engineering for Industry, Vol. 116,

May. 1994, p. 166.

25. M.E. Mueller, R.E. DeVor, and P.K.Wright, Transactions of the 25th North American Manu-

facturing Research Institution, 25, 123, (1997)

26. Carslaw, H., S., and Jaeger, J.C., Conduction of Heat in Solids, 2nd. Edition, Oxford Univer-

sity Press, London.

27. Tay, A.O., Stevenson, M.G., de Vahl Davis, G., Proc. Inst. of Mech. Engrs., 188, 627, (1974)

28. Courant, R., Bulletin of the American Mathematics Society, 49, (1943)

29. Black, J.T., Personal communications, Auburn University, (1998)

30. Athavale, S.M., and Strenkowski, J.S., Proceedings of the CIRP International Workshop on

Modeling of Machining Operations, held in Atlanta, GA., Published by the University of

Kentucky Lexington, KY, 40506-0108. p.203, (1998)

31. Rakotomalala, R., Joyot, P., and Touratier, M., Communications in Numerical Methods

Engineering, 9, 987, (1993)

32. Marusich, T.D. and Ortiz, M., International Journal for Numerical Methods in Engineering,

38, 3675, (1995)

33. Lin, Z.C., and Lin, S.Y., Journal of Engineering Materials and Technology, 114, 218 (1992)

34. Caroll J.T. III, and Strenkowski, J.S., International Journal of Mechanical Sciences, 30,

(12), 899 (1988)

35. Obikawa, T. and Usui, E., Journal of Manufacturing Science and Engineering, 118, 208

(1996)

36. Maekawa, K. and Shirakashi, T., Journal of Engineering Manufacture, 120, (B3), 233

(1996)

37. Strenkowski, J.S. and Athavale, S.M., Journal of Manufacturing Science and Engineering,

119, 681 (1997)

38. Zhang, B., and Bagchi, A., Proc. of ASME IMEC, Material Issues in Machining and Physics

of Machining Processes, II, Eds. Stephenson, D.A., and Stevenson, R., p. 157, (1994).

39. Obikawa, T., Sasahara, H., Shirakasi, T. and Usui, E., Journal of Manufacturing Science and

Engineering, 119, 667 (1997)

40. Lin, Z-.C., International Journal of Machine Tools and Manufacture, 34, (6), 770 (1994)

41. Sekhon, G.S., and Chenot, J.L., Engineering Computations, 10, 31 (1993)

42. Engelmann, B., Mechanical Engineering, 113, (3), 48 (1991)

43. Yang, J. A., A predictive Model of Chip Breaking for Groove-Type Tools in Orthogonal

Machining of AISI 1020 Steel, Ph.D. Dissertation, North Carolina State University, (1992)

44. Masud, A., A Space-Time Finite Element Method for Fluid-Structure Interaction, Ph.D. Dis-

sertation, Stanford University, (1993).

45. Chen, G.Z., and Black, J.T., Manufacturing Review, 7, (2), 120 (1994)

46. Jawahir, I.S., Balaji, A.K., and Stevenson, R., Editors of the Proceedings of the CIRP Inter-

national Workshop on Modeling of Machining Operations, held in Atlanta, GA., Published

by the University of Kentucky Lexington, KY, 40506-0108. (1998)

47. Klamecki B. E., Incipient Chip Formation in Metal Cutting-A Three-Dimension Finite Ele-

ment Analysis, Ph.D. dissertation, University of Illinois at Urbana-Champaign, (1973)

48. Tay, A.O., Stevenson, M.G., and de Vahl Davis G., Proceedings of the Institute of Mechani-

cal Engineers, 188, 627 (1974)

408 MODELING OF METAL CUTTING

49. Oxley, P.L.B., and Stevenson, M.G., Journal of the Institute of Metals, 95, 308 (1967)

50. Tay, A.O., Stevenson, M.G., de Vahl Davis, G., and Oxley, P.L.B., International Journal of

Machine Tool Design and Research, 16, 335 (1976)

51. Stevenson, M.G., Wright, P.K., and Chow, J.G., J. of Eng. for Industry (ASME), 105, 149

(1983)

52. Muraka, P.D., Barrow, G., and Hinduja S., International Journal f Mechanical Science, 21,

445 (1979)

53. Childs, T.H.C., Maekawa, K., and Maulik, P., Materials Science and Technology, 4, 1006

(1988)

54. Maekawa, K., Nakano Y., and Kitagawa, T., JSME, International Journal, Series C, 39, (4)

(1996)

55. Komvopoulos, K., and Erpenbeck, S.A., Journal of Engineering for Industry, 113, 253

(1991)

56. Lin, Z.C., and Lin, S.Y., Journal of Engineering Materials and Technology, 114, 218 (1992)

57. Strenkowski, J.S., and Moon K.-J., Journal of Engineering for Industry, 112, 313, (1990)

58. Zienkiewicz, O.C., Jain, P.C., and Onate, E., International Journal of Solids and Structures,

14, 15 (1978)

59. Strenkowski, J.S., and Hiatt, D., Fundamental Issues in Machining (ASME) PED Vol. 43, 67

(1990)

60. Broek D., Kluwer Academic Publishers, Dordrecht, The Netherlands, (1988)

61. Strenkowski, J.S., Larson, and Chern, ASME Tribological Aspects in Manufacturing PED

Vol . 54 and TRIB Vol. 2, 279 (1991)

62. Bettess, P., International Journal of Numerical Methods in Engineering, 11, 53 (1977)

63. Jawahir, I.S., Dillon, O.W., Balaji, A.K., Redetzky, M., and Fang, N., Proceedings of the

CIRP International Workshop on Modeling of Machining Operations, held in Atlanta, GA.,

Published by the University of Kentucky Lexington, KY, 40506-0108. p.161, (1998)

64. Kistler, B.L. Finite Element Analyses of Tool Stresses in Metal Cutting Processes Sandia

Report, SAND-97- 8224, UC-406 (1997)

65. Bammann, D.J., Chiesa, M.L., McDonald, M.L., Kawahara, W.A., Dike, J.J., and Revelli,

V.D., AMD, 107, 7 (1990)

66. El-Wardany, T.I., Mohammed, E., and Elbestawi, M.A., International Journal of Machine

Tools and Manufacture, 36, (5), 611 (1996)

67. Park, I.W. and D.A. Dornfeld, “A Study of Burr Formation Processes using the Finite Ele-

ment Method - Part I and II”, Trans. ASME, J. Eng. Mats., to appear, 1999

68. C.C. Hayes, S. Desa and P.K. Wright, American Society of Mechanical Engineers, Winter

Annual Meeting, Special Bound Volume on Concurrent Product and Process Design, Decem-

ber 1989, San Francisco, CA., DE-Vol. 21 and PED-Vol. 36, 87 (1989)

69. Boothroyd, G., Fundamentals of Metal Machining and Machine Tools, Marcel Dekker

(1990)

70. D.W. Yen and P.K. Wright, Trans. ASME, Journal of Engineering for Industry, 108, 252

(1986)

71. J.G. Chow and P.K. Wright, Trans. ASME, Journal of Engineering for Industry, 110, (1), 56

(1988)

REFERENCES 409

72. Lajczok, M.R., A Study of some Aspects of Metal Machining Using the Finite Element

Method, Ph.D. dissertation, North Carolina State University, Mechanical and Aerospace

Engineering Dept., (1980)

73. Usui, E., and Shirakashi, T., ASME Publication PED, 7, pp. 13 (1982)

74. Iwata, K., Osakada, K., and Terasaka, Y., Journal of Engineering Materials and Technology,

106, 132 (1984)

75. Liu, C.R., Lin, Z.C., and Barash, M.M., High Speed Machining, 167 (1984)

76. Liu, C.R., Lin, Z.C., and Barash, M.M. High Speed Machining, 181 (1984)

77. Klamecki, B.E., and Kim, S., Journal of Engineering for Industry, 110, 322 (1988)

78. Howerton D.H., and Strenkowski, J.S., Transactions of the North American MAnufacturing

Research Conference, 17, 95 (1989)

79. Strenkowski, J.S. and Lin, J.C., Manufacturing Science and Engineering MED Vol. 4, 273,

(1996)

80. Strenkowski, J.S. and Carroll, J.T., Journal of Engineering for Industry (ASME), 107, 349

(1985)

81. Strenkowski, J.S. and Carroll, J.T., Manufacturing Processes Machines and Systems (Pro-

ceedings of the 13th NSF Conference on Production Research), 261 (1986)

82. Carroll, J.T., and Strenkowski, J.S., International Journal of Mechanical Sciences, 30, (12)

899 (1988)

83. Stori, J.A., and Wright, P.K., http://madmax.me.berkeley.edu/webparam/demo2/

summary2.html

84. Komanduri, R., and Hou, Z.B., “Thermal Modeling of the Metal Cutting process” - to be

published in the International Journal of Mechanical Sciences (1999/2000)

85. Komanduri, R., and Raff, L.M.,” Molecular Dynamics (MD) Simulation of Machining” - to

be published in the Proceedings of the Institution of Mechanical Engineers, London (2000)

86. Ueda, K., Fu, H., and Manabe, K., Machining Science and Technology, 3, 1, 61 (1999)

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CHAPTER 13 MANAGEMENT OF

TECHNOLOGY

13.1 RETROSPECTIVE AND PERSPECTIVE

In his book Man the Tool Maker, published by the British Museum, Kenneth P. Oakley states

the proposition: “Human progress has gone step by step with the discovery of better materials of

which to make cutting tools, and the history of man is therefore broadly divisible into the Stone

Age, the Bronze Age, the Iron Age, and the Steel Age”.

Certainly, in the last 100 years - since the invention of high speed steels by Taylor and White -

the accelerated development of new materials for cutting tools has made the most important

contribution to machining efficiency. Despite the considerable advances in the machine tools,

historical events show that the discovery of a new “genus” of cutting tool is the “trigger” for

advances. High speed steels were so revolutionary in their time that the machinery industry had

to “scramble” to respond. Similarly, with the broader use of cemented carbides in the 1940s, and

ceramics in the 1970s, the machine tool industry was, in each era, faced with the pressure to

build stiffer and faster machines with greater spindle speeds. NC machines, invented in 1950,

allowed efficiencies of another kind, but the higher speeds made possible by each era of tool

material still remained, and will continue to remain, the primary driver for the overall design of

lathes and milling machines.

The ability of any type of cutting tool to withstand the stresses and temperatures at its edge is

the key factor limiting the speed of cutting and the forces and power which can be employed. A

primary research goal is therefore to obtain an increase in the high temperature strength of any

tool material. However, this must be balanced against secondary effects, particularly, impact

strength and diffusion resistance.

How will Kenneth Oakley’s quote apply in the next 100 years?

• From the point of view of the work material, the consumer demands of high speed travel

will continue to extend the Steel Age into the Space Age. Titanium, nickel, and high-silicon

412 MANAGEMENT OF TECHNOLOGY

aluminum alloys are now commonplace in the aerospace and automobile industries.

Machining science and technology must respond to these alloys, no matter how difficult

they are to machine.

• From the point of view of the tool material, there seems to be an ironical return to the Stone

Age, as diamond, ceramic and other non-metallic materials slowly gain acceptance. These

have a very high strength at elevated temperatures. However, while these have been com-

mercially available for some time, it is only recently that their consistency has improved,

leading to a market demand in relation to the cemented carbides. In addition to consistent

performance, there is also the question of cost vs. performance. In this Chapter 13 on Con-

clusions, it worth discussing this point in some more detail below.

13.2 CONCLUSIONS ON NEW TOOL MATERIALS

The commercialization of new tool materials, all too often, falls into a category described by

Busch and Dismukus as the “advanced technology trap”. “In these situations, the new high per-

formance technology is developed with the intention of having it “substitute” for an incumbent,

lower performance alternative and, while the performance of the new technology is measurably

better, unfortunately it is also significantly more expensive. Undaunted, the commercialization

activities proceed assuming that the market-place will recognize the performance advantages

and be willing to pay for them. Too often, this is simply not the case”.

The developers of cutting tools are always faced with this substitution paradigm. The poten-

tial market users - the machine shops - are already engaged in a satisfactory business and they

are not “in desperate need” of a new invention. Invention is the other paradigm of commercial-

ization, where a new product could not exist without the invention of a new material. One obvi-

ous example includes the transistor: in 1947, Shockley, Brattain and Bardeen and the directors

at Bell Labs., recognized the need for this invention to replace the troublesome vacuum tube.

And once invented, the transistor spurred the integrated circuit, the microprocessor and all ele-

ments of today’s computerized society.

Thus, when a material is commercialized through invention, it becomes, or is a critical part of,

a new product or system that enables or provides unique and valued functionality. By contrast,

in substitution-based commercialization, new materials technologies compete on the basis of

their cost and performance relative to the existing products. The technology offering the best

balance of cost and performance will be the winner in the market place. Thus ceramic or dia-

mond-coated cutting tools may well continue to displace tungsten carbide. But only where and

when the balance of cost and performance is considered favorable to that of the carbides.

What are the market forces that might displace cemented carbide in favor of ceramic or dia-

mond? For the especially troublesome machining problems - with aerospace nickel, titanium or

high-silicon aluminum - the cutting tool salesperson will be welcomed by most users to carry

out some trials on their machinery. And in today’s competitive environment, most machine

shops will still be willing to try new designs, coatings (and coolants) hoping for improved per-

formance. But how much are they willing to pay?

Experience has shown that the willingness to pay for the improved performance is consis-

tently overestimated by those in charge of technology commercialization. In most commercial

markets, a new technology will not replace an existing one, even if the price increase is only